基于灰度梯度和纹理色度模式的图像检索:卫星图像检索

IF 0.5 4区 计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING International Journal of Data Warehousing and Mining Pub Date : 2021-01-01 DOI:10.4018/IJDWM.2021010104
I. Jacob, P. Betty, P. Darney, Hoang Viet Long, T. Tuan, Y. H. Robinson, S. Vimal, E. G. Julie
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引用次数: 2

摘要

检索图像的方法包括利用数据库的特征从数据库中检索图像。它们是颜色、形状和质地。这些特征用于查找查询图像与数据库中图像的相似度。图像按照这种相似性排序。本文使用纹理内部和纹理间的色度及其强度。本文采用局部对抗彩色纹理模式(LOCTP)提取颜色间纹理特征。局部二值模式(LBP)给出纹理内部信息。利用定向梯度直方图(HoG)从卫星图像中获取形状信息。利用土地覆盖遥感数据库、NWPU-VHR-10数据集和卫星光学土地覆盖数据库进行性能分析,结果优于以往的工作。
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Image Retrieval Using Intensity Gradients and Texture Chromatic Pattern: Satellite Images Retrieval
Methods to retrieve images involve retrieving images from the database by using features of it. They are colour, shape, and texture. These features are used to find the similarity for the query image with that of images in the database. The images are sorted in the order with this similarity. The article uses intra- and inter-texture chrominance and its intensity. Here inter-chromatic texture feature is extracted by LOCTP (local oppugnant colored texture pattern). Local binary pattern (LBP) gives the intra-texture information. Histogram of oriented gradient (HoG) is used to get the shape information from the satellite images. The performance analysis is land-cover remote sensing database, NWPU-VHR-10 dataset, and satellite optical land cover database gives better results than the previous works.
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来源期刊
International Journal of Data Warehousing and Mining
International Journal of Data Warehousing and Mining COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.40
自引率
0.00%
发文量
20
审稿时长
>12 weeks
期刊介绍: The International Journal of Data Warehousing and Mining (IJDWM) disseminates the latest international research findings in the areas of data management and analyzation. IJDWM provides a forum for state-of-the-art developments and research, as well as current innovative activities focusing on the integration between the fields of data warehousing and data mining. Emphasizing applicability to real world problems, this journal meets the needs of both academic researchers and practicing IT professionals.The journal is devoted to the publications of high quality papers on theoretical developments and practical applications in data warehousing and data mining. Original research papers, state-of-the-art reviews, and technical notes are invited for publications. The journal accepts paper submission of any work relevant to data warehousing and data mining. Special attention will be given to papers focusing on mining of data from data warehouses; integration of databases, data warehousing, and data mining; and holistic approaches to mining and archiving
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